Welcome to the online course on Data Visualization.
Data Visualization is a graphical representation of information and data.
Data Visualization tools provide an accessible way to see and understand data because with visualizations it becomes easier for the human brain to understand and pull insights out of the data.
The primary goal of a data analyst is to increase efficiency and improve performance by discovering patterns in data.
In this course, you will get advanced knowledge on Data Visualization.
This course begins with providing you the complete knowledge on Python programming language.
You will learn all the concepts of python programming required.
This course will cover:-
- Python variables
- Python data types
- Loops and Conditionals
- Strings
- Regular Expressions
- Data Time Objects
- Numpy Library
- Pandas Library
Along with the python programming, this course will cover the concepts of query analysis as well.
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That will help you become an expert in analyzing data using different libraries such as Dabl and sweetviz.
Not only this, this course will provide advanced knowledge on Data Visualization.
This course will cover:-
Basic data visualization
Advanced Data Visualization such as facet grids, polar charts, waffle charts, maps, statistical charts etc.
Animated Data Visualization such as bubble plot, facets, scatter maps and choropleth maps.
Some miscellaneous charts such as sunburst charts, parallel-coordinate charts, gantt charts etc.
All this in one course!!!!
Along with all these we have three bonus projects for you!
Startup Case study and Analysis
Player performance Reviewer and
IPL Data Science Analyzer
Where you will apply all the concepts learnt in this course.
This course is a complete package.
Lots and lots of quizzes and exercises are waiting for you.
You will also have access to all the resources used in this course.
Instructor Support – Quick Instructor Support for any queries.
I’m looking forward to see you in the course!
Enroll now and become an expert in data visualizations.
Python Fundamentals
Why should you learn Python?
Installing Python and Jupyter Notebook
Understanding the Interface of Jupyter Notebook
Q and A
Naming Convention for variables
Built in Data Types and Type Casting
Scope of Variables
Quiz on Variables and Data Types
Quiz Solution
Exercise on Variables and Data Types
Solution on Variables and Data Types
Exercise on Scope of Variables
Solution on Scope of Variables
Arithmetic and Assignment Operators
Comparison, Logical, and Bitwise Operators
Identity and Membership Operators
Quiz on Operators
Quiz Solution
Exercise on Arithmetic and Assignment Operators
Solution on Arithmetic and Assignment Operators
Exercise on Comparison, Logical, and Bitwise Operators
Solution on Comparison, Logical, and Bitwise Operators
Exercise on Identity and Membership Operators
Solution on Identity and Membership Operators
String Formatting
String Methods
User Input
Quiz on Strings
Quiz Solution
Exercise on Strings
Solution on Strings
If, elif, and else
For and While
Break and Continue
Quiz on Loops and Conditionals
Quiz Solution
Exercise on Loops and Conditionals
Solution on Loops and Conditionals
Python for Data Science
Introduction to datetime
Date and Time Class
Datetime Class
Timedelta Class
Quiz on Dates and Times
Quiz Solution
Exercise on Date and Time
Solution on Date and Time
Meta Characters for Regular Expressions
Built-in Functions for Regular Expressions
Special Characters for Regular Expressions
Sets for Regular Expressions
Quiz on Regular Expressions
Quiz Solution
Exercise on Regular Expressions
Solution on Regular Expressions
Array Creation using Numpy
Mathematical Operations using Numpy
Built-in Functions in Numpy
Quiz on Introduction to Numpy
Quiz Solution
Exercise on Built-in Functions in Numpy
Solution on Built-in Functions in Numpy
Reading Datasets using Pandas
Plotting Data in Pandas
Indexing, Selecting, and Filtering Data using Pandas
Merging and Concatenating DataFrames
Lambda, Map, and Apply Functions
Quiz on Introduction to Pandas
Quiz Solution
Exercise on Pandas Plotting
Solution on Pandas Plotting
Exercise on Indexing and Selecting
Solution on Indexing and Selecting
Exercise on Apply Functions
Solution on Apply Functions
Data Visualization
Univariate Analysis
Bivariate Analysis
Multivariate Analysis
Quiz on Basics of Visualization
Quiz Solution
Scatter Plots
Charts with Colorscale
Bar, Line, and Area Charts
Facet Grids
Statistical Charts
Polar Charts
Subplots
3D Charts
Waffle Charts
Maps
Quiz on Advanced Visualizations
Quiz Solution
Animation with Bubbleplot
Animation with Facets
Animation with Scatter Maps
Animation with Choropleth Maps
Quiz on Animated Visualizations
Quiz Solution
Introduction to Ipywidgets
Interactive Univariate Analysis
Interactive Bivariate Analysis
Interactive Multivariate Analysis
Quiz on Interactive Visualizations
Quiz Solution
Sunburst Charts
Parallel Co-ordinate Charts
Funnel Charts
Gantt Charts
Ternary Charts
Tree Maps
Network Charts
Quiz on Miscellaneous Charts
Quiz Solution
Query Analysis
Aggregate functions used for Grouping
Using Groupby for Grouping Operations
Groupby with Idxmax and Idxmin functions
Using Color scales for better visualization
Visualizing the Groupby Operations
Using Pivot Tables for Grouping Operations
Difference between Groupby and Pivot tables
Performing Cross Tabulation
Visualizing Cross tabulated Data
Interactive Grouping Operations
Quiz on Grouping Operations
Quiz Solution
When to perform Filtering Operations
Introduction to Simple Filtering Operations
Advanced Filtering Operations
Filtering and Grouping Operations
Interactive Filtering Operations
Quiz on Filtering Operations
Quiz Solution
Startups Case Study and Analysis
Understanding the Problem Statement
Setting up the Environment
Data Cleaning
Querying the data using Visualizations Part – 1
Querying the data using Visualizations Part – 2
Major Learning from Data
Quiz on Startups case study and analysis
Player’s Performance Reviewer
Understanding the problem statement
Setting up the Environment
Data Cleaning
Feature Engineering
Data Visualization
Query Analysis
Major Learnings from the project
Quiz on Players Performance Analysis
IPL Data Science Analyzer
Setting up the Environment
Understanding the Dataset
Understanding the Problem Statement
Summarizing Interesting Facts from the Data
Exploring the Best Players from IPL
Discovering the Biggest Matches in IPL
Understanding the Match Results
Uncovering the Most Popular IPL Seasons and Teams
Realizing the Locations for all the IPL Seasons
Comparing Toss Winners and Winners
Checking the Winning Locations for all the Teams
Analyzing Toss Decisions in IPL Matches
What is DL in an IPL Match?
Key Insights from this Project
Quiz on IPL Data Science Analyzer
Outro Section
Conclusion
How to Get Your Certificate of Completion
Bonus Section
Bonus Lecture
